Global Energy Trends

INFO 523 - Project Final

The goal of this project is to analyze the complex relationships between economic and population growth, sustainable energy practices, and energy consumption
Author
Affiliation

data detectives - Ayesha, Abhishek, Sheemithra, Toluwanimi, Valerie, Alyssa

School of Information, University of Arizona

Abstract

This project utilizes the comprehensive energy dataset from Our World in Data, spanning from 1900 to 2022, to examine the global energy consumption trends regarding economic growth, population dynamics, and the adoption of sustainable energy practices. The primary goal of the project is to design a predictive dashboard that models a nation’s energy consumption based on essential factors such as population size, GDP, and the proportion of electricity derived from renewable sources. The analysis will utilize a range of statistical and machine learning techniques, including time series decomposition, linear regression for key predictors, and regression analysis. We will evaluate the performance of these regression models using R-squared and Root Mean Squared Error (RMSE) metrics to gauge their accuracy and explanatory power. This evaluation is essential for enhancing predictive accuracy and reliability in energy policy formulation and planning. The project will analyze trends in the use of renewable energy at the regional and national levels, with a certain emphasis on emphasizing countries that lead the way in sustainable energy practices and those making progress toward lower greenhouse gas emissions. This analysis will provide crucial insights for industry and researchers dedicated to promoting energy sustainability and promoting economic growth.

Question 1

Is it possible to predict a nation’s power consumption by considering its population size, gross domestic product (GDP), and the percentage of electricity generated from renewable sources and changes across the years?

A right-skewed distribution is shown by the distribution’s shape, which shows that most values are low and that frequency drops off quickly as values rise. In order to train the model to identify patterns and to validate its correctness by contrasting the projected values with the actual data shown in the plot, the historical data represented in this histogram would be crucial.

The relationship between primary energy consumption and the GDP, population size, and the proportion of power derived from renewable sources is represented graphically in this plot. If persistent patterns are seen over time, one might utilize the spread and trends of the scatter points to deduce that there might be correlations between these parameters and a country’s power consumption, which could be used to anticipate energy usage.

Indicating possible relationships between these variables and a country’s power consumption, the graphic shows how the population, GDP, percentage of renewable electricity, and energy consumption have changed over time. For example, if GDP and population growth are accompanied by an upward trend in the primary energy consumption curve, this could indicate that energy demand is driven by economic activity and demographic growth. On the other hand, while a rise in the proportion of power derived from renewable sources may not necessarily translate into reduced energy usage, it may suggest a change in the composition of energy sources. Time periods in which the growth of energy consumption slows down or deviates from trends in GDP and population may be linked to advancements in energy efficiency or structural adjustments in the economy. examining these patterns and connections across time. In order to forecast future power consumption patterns, data demonstrating a high historical association between these variables can be used to develop a predictive model through the analysis of such trends and relationships across time.

Question 2

What countries or regions are engaging in sustainable energy practices and relying more on renewable energy compared to nonrenewable energy? Which countries are moving towards the trajectory of relying more on renewable energy and producing less greenhouse gas emissions?

  • The animated map shows the share of renewable energy consumption over two decades, from 2000 to 2022. Playing the animation reveals the countries that have the highest share of renewable consumption, such as China, India, Brazil, and the United States. Over the two decades, there hasn’t been much change in countries that practice renewable energy
  • The second plot explores the country with the highest mean renewable energy share. China has the highest renewable share of 22.75%, followed by India with 14.23%, Brazil and the United States with 9% and 3.28% respectively. Over the decade, the top countries remain the same, with only the top country’s percentage share increasing, indicating that they are working more towards renewable practices

Top 10 Countries with Highest Mean Greenhouse Gas Emissions

  • This plot shows the mean greenhouse gas emissions over two decades. Here, China, despite practicing more renewable energy consumption and share, emits the most greenhouse gases. The same trend is seen for the United States and India

Data Wrangling for Density Plot

Density plots for renewable and non-renewable energy for the continents

Renewables Consumption Plot

Visualizing the density plots for renewable consumption

Non-renewables consumption Plot

The visualization for the non-renewable consumption of energy

Repo Organization

The following folders comprise the project repository

  • .github/: This directory is designated for files associated with GitHub, encompassing workflows, actions, and templates tailored for issues.

  • _extra/: Reserved for miscellaneous files that don’t neatly fit into other project categories, providing a catch-all space for various supplementary documents.

  • _freeze/: Within this directory lie frozen environment files containing comprehensive information regarding the project’s environment configuration and dependencies.

  • data/: Specifically allocated for storing i data files crucial for the project’s functionality, encompassing input files, datasets, and other essential data resources.

  • images/: Serving as a repository for visual assets employed throughout the project, including diagrams, charts, and screenshots, this directory maintains visual elements integral to project documentation and presentation.

  • .gitignore: This file functions to specify exclusions from version control, ensuring that designated files and directories remain untracked by Git, thus streamlining the versioning process.

  • README.md: Serving as the primary hub of project information, this README document furnishes essential details encompassing project setup, usage instructions, and an overarching overview of project objectives and scope.

  • _quarto.yml: Acting as a pivotal configuration file for Quarto, this document encapsulates various settings and options governing the construction and rendering of Quarto documents, facilitating customization and control over document output.

  • about.qmd: This Quarto Markdown file supplements project documentation by providing additional contextual information, elucidating project purpose, contributor insights, and other pertinent project details.

  • index.qmd: index.qmd: This serves as the main documentation page for our project. This Quarto Markdown file provides detailed descriptions of our project, including all code and visualization.